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eCornell’s New Data Analytics Certificate Equips Professionals to Translate Big Data into Actionable Business Insights

— Program is essential step in data science career, ranked best job in America for 2017 —

Data scientists and data analysts are hot commodities; they were ranked the #1 job in America for 2017 by Glassdoor and named the sexiest job of the 21st century by Harvard Business Review. Demand for these roles—and their intersecting skills in business, statistics, and programming—is driven by organizations swimming in data but hamstrung by a shortage of employees with the critical mindset needed to translate it into meaningful decisions. Yet educational institutions lag in preparing students for these jobs. To close the gap, Cornell University is now offering professionals the opportunity to earn an executive certificate in Data Analytics so they can build core fluency in data analysis and a foundation for further technical study.

“Data analysis requires professionals to be informed consumers of data. Technical knowledge is necessary, but it’s actually even more valuable to know which questions to ask, how to ask them, test them, and translate them into business intelligence. Done well, data analysis provides a valid narrative business leaders can follow to make more successful strategic decisions,” said Chris Anderson, Ph.D., the certificate’s faculty author from Cornell University.

The Data Analytics certificate consists of three intensive courses that provide professionals with an essential understanding of how and why data is used to create value in business: Understanding and Visualizing Data, Implementing Scientific Decision-Making, and Using Predictive Data Analysis. Each three-week course builds the analytical mindset, starting with what data is, and moving into how to visualize data and build predictive models and reporting. Students strengthen their ability to connect data to decisions—learning how to make inferences about data samples and analyze relationships across data to predict future outcomes, with the option to use datasets from their own companies.

Courses offer step-by-step “How Tos” for all statistical processes and teach universal Excel-based analysis tools. From data visualization to predictive analytics, Professor Anderson combines accessible terminology with his wide-ranging experience in management science and statistics to teach skills that translate across software platforms.

The Data Analytics certificate is a critical credential for today’s professionals across many industries, complementing several eCornell certificate programs in marketing, leadership, revenue management, and human resources. For students new to statistics, courses expose them to the fundamentals and remove barriers to getting started. Professionals with deeper statistical knowledge will learn to ground data in the language of business decisions, and current data analysts will enhance their ability to communicate with key audiences and make meaning out of data. Senior executives will also become more critical consumers of data, and better able to guide and manage analysts productively.

Students who complete the program receive an Executive Certificate from Cornell University and will earn 0.6 Professional Continuing Education Units (CEUs) for each course completed.

 

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About eCornell
As Cornell University’s online learning unit, eCornell delivers online professional certificate courses to individuals and organizations around the world. Courses are personally developed by Cornell faculty with expertise in a wide range of topics, including hospitality, management, marketing, human resources and leadership.  Students learn in an interactive, small cohort format to gain skills they can immediately apply in their organizations, ultimately earning a professional certificate from Cornell University. eCornell has offered online learning courses and certificate programs for 15 years to over 130,000 students at more than 2,000 companies.

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Determine Your Customer Lifetime Value

Marketing is all about maximizing a customer’s financial contribution to your brand. The more a customer spends on your products or services, the better it is for your bottom line. But there’s more that goes into a customer’s value than a big purchase here and there. We’ve taken the formula for determining your customer’s lifetime value from our certificate in Data-Driven Marketing to give you a sneak peek into the Ivy League strategies we can offer to enhance your marketing campaign.

Customer Lifetime Value Equation

You can use a simple equation to determine exactly how valuable a customer is to your overall success as a company. By figuring out the customer lifetime value (CLV) for your top customers, you’ll be able to see just how much each contributes to your revenue goals.

The customer lifetime value calculation consists of three distinct parts, which are multiplied to give you a quantifiable figure that shows a customer’s overall worth. Use this formula to see how your top customers shape up or to analyze a specific segment to see how certain customers can become more valuable.

Average Spend

The first part of the equation is simple. How much does a given customer spend, on average, when he or she patronizes your business? This number can be easily calculated through any sort of internal database you may have. You can also help to drill down to the individual customer by using customer loyalty cards or personalized website logins for online purchases.

Repeat Sales

Knowing how much a customer spends is only valuable if placed in the right context. A customer who spends $1,000 for a one-time purchase is less valuable than someone who spends $100 each month over the course of a year. While you obviously want a customer to spend as much as possible, the frequency with which a customer shops is just as important. Furthermore, frequent visits show a measure of loyalty that can’t be quantified by looking solely at a customer’s average expenditure.

Retention Time

Let’s face it, there’s no such thing as a lifelong customer. You’d be foolish to expect a customer to stick around forever. But you can figure out how long the average customer supports your business and apply that to the general population. Again, the longer the retention time, the better off you are, but some businesses aren’t based around lengthy periods of retention. For example, a store that specializes in baby merchandise won’t be able to retain customers for as long as a store that targets adults.

When you multiply all three of these elements, you end up with a figure that can be used to represent a customer’s lifetime value to your business. This amounts to the present value of future cash flows, so you may end up getting more out of customers than you expect. In any case, customer lifetime value is a great tool to use as you attempt to identify and target your most important customers.

Alternate Calculations

The calculation described above is just one way to calculate CLV. Other formulas incorporate additional factors, such as acquisition costs, direct mailing costs, and your company’s margin rate.

If you’re interested in learning more about CLV and other marketing concepts, consider the Data-Driven Marketing certificate program offered by eCornell. You’ll learn about the elements that comprise customer lifetime value, as well as how it can best be used as part of a comprehensive marketing campaign.

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Insightful Big Data Conversations

Many executives have read the articles touting that 90% of all data has been created in the past several years. While this news is exciting for data junkies like me, I think it opens the door to a new set of conversations executives and CEOs must have as Big Data and analytics become increasingly accessible and abundant. There are two conversations executives need to have to ensure they are on the right path toward data insight rather than data overload.

“Can we use Big Data to help drive better decision making?”  

According to McKinsey & Company, the productivity and profitability of firms that use Big Data and analytics is 5-6% higher than those of peer firms. However, the best analytics dashboard in the world means nothing if frontline employees do not use it to make informed decisions. It’s important to assess current skills, culture, and decision-making processes while planning any Big Data strategy.

“What data do we have and what data do we need?” 

Many executives mistakenly think the data they have in-house is all they will need. In most cases, data from outside sources add contextual insights that are simply nonexistent otherwise. The focus should be on the quality and relevance of each data point to address business challenges.

While Big Data and analytics can provide a lasting competitive advantage, the most important aspect of any Big Data and analytics initiative rests in the insights gleaned through the data. Once you have the insights, you can focus your attention on making those insights actionable.

 

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3 Reasons Big-Data Has Big Relevance

To those who challenge the significance of big-data, I say, “Get real.”

Big-data absolutely matters for analytics and related disciplines such as market research and competitive intelligence. Why? Because it offers distinct benefits that can otherwise be hard, or even impossible, to come by.

I’m sure you’ve heard big-data described in terms of size, variety, and velocity, or what I call “real-timeliness.”Read More

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Search Heats Up

Keep your eye on the world of Search.  There is deep interest from the user and the investment community in expanding the world of Search.  Google continues to add new functionality. Twitter users are developing a broad set of search tools aimed at harvesting knowledge from one’s social network.  Microsoft has rebranded their search as Bing and poured resources.  And, my inbox is filled with recent venture notices from engines like Wowd and Yebol.  Instructional designers will need to do some deep thinking about the role of Search in learning architectures.

From Elliott Masie’s Learning TRENDS #580, June 8, 2009: